Advancements and applications of X-ray microcomputed tomography and digital image processing for the characterization of asphaltic materials
DOI:
https://doi.org/10.58922/transportes.v31i1.2854Keywords:
X-ray micro-computed tomography, Digital image processing, Asphaltic materials, Artificial intelligenceAbstract
This paper presents recent advances of the application of the x-ray microtomography (micro-CT) technique in the characterization of asphaltic materials. Imaging characteristics to perform micro-CT tests of asphalt concrete and fine aggregate matrix mixtures are discussed. A procedure developed to perform the digital image processing of the asphaltic materials images is also presented. The key findings from this paper are: (1) spatial resolutions between 10 µm/pixel and 13 µm/pixel are adequate to perform the evaluation of asphaltic material volumetrics; (2) instead of thresholding, the U-Net architecture can be used to optimize the digital image processing; (3) a representative volume element comprising 33% of the sample volume can be adopted for volumetric evaluations of asphaltic materials; (4) fine aggregate matrix volumetric properties are dependent on the asphalt mixture volumetrics.
Downloads
References
Al-Raoush, R. e A. Papadopoulos (2010) Representative Elementary Volume Analysis of Porous Media using X-ray Computed Tomography. Powder technology, v. 200(1-2), p. 69-77. DOI: 10.1016/j.powtec.2010.02.011. DOI: https://doi.org/10.1016/j.powtec.2010.02.011
Amelian, S.; Y. R. Kim; P. H. Osmari; F. T. S. Aragão; D. Braz e R. F. Costa (2019) Development of a Volumetric Mix Design Approach for Fine Aggregate Matrix (FAM) and Validation with Micro-CT Method, Transportation Research Board 98th Annual Meeting. Washington, D. C., Estados Unidos.
Augusto, K. (2016) Microtomografia Computadorizada de Raios X Aplicada à Caracterização de Porosidade em Pelotas de Minério de Ferro. Tese de Doutorado. PUC-Rio. Rio de Janeiro.
Bezerra, E.; K. Augusto e S. Paciornik (2020) Discrimination of Pores and Cracks in Iron Ore Pellets using Deep Learning Neural Networks. REM - International Engineering Journal, v. 73, n. 2, p. 197-203. DOI: 10.1590/0370-44672019730119. DOI: https://doi.org/10.1590/0370-44672019730119
Buades, A.; B. Coll e J-M. Morel (2011) Non-Local Means Denoising. Image Processing On Line Journal, v. 1, pp. 208-212. DOI: 10.5201/ipol.2011.bcm_nlm. DOI: https://doi.org/10.5201/ipol.2011.bcm_nlm
Chen, A.; G. D. Airey; N. Thom e Y. Li (2022) Characterisation of Fatigue Damage in Asphalt Mixtures using X-ray Computed Tomography. Road Materials and Pavement Design, p. 1-19. DOI: 10.1080/14680629.2022.2029756. DOI: https://doi.org/10.1080/14680629.2022.2029756
CTAnalyzer v. 1.20.3. (2020) [Computer software]. Bruker, Kontich, Belgium. Disponível em <https://www.bruker.com/en/products-and-solutions/preclinical-imaging/micro-ct/3d-suite-software.html > (acesso em 01/09/2022).
Dragonfly 2020.1. (2020) [Computer software] Object Research Systems (ORS) Inc, Montreal, Canadá. Disponível em <http://www.theobjects.com/dragonfly> (acesso em 01/09/2022).
Enríquez-León, A. J.; T. D. Souza, F. T. S. Aragão; D. Braz; A. M. B. Pereira e L. P. Nogueira (2021a) Determination of the Air Void Content of Asphalt Concrete Mixtures using Artificial Intelligence Techniques to Segment Micro-CT Images. International Journal of Pavement Engineering, p. 1-10. DOI: 10.1080/10298436.2021.1931197. DOI: https://doi.org/10.1080/10298436.2021.1931197
Enríquez-León, A. J.; T. D. Souza; F. T. S. Aragão; A. M. B. Pereira e L. P. Nogueira (2021b) Characterization of the Air Void Content of Fine Aggregate Matrices within Asphalt Concrete Mixtures. Construction and Building Materials, v. 300, 124214, p. 1-12. DOI: 10.1016/j.conbuildmat.2021.124214. DOI: https://doi.org/10.1016/j.conbuildmat.2021.124214
Gomes, O. D. M. (2007) Microscopia Co-Localizada: Novas Possibilidades na Caracterização de Minérios. Tese de Doutorado. PUC-Rio, Rio de Janeiro.
Gonzales, R. C. e R. E. Woods (2010) Processamento Digital de Imagens, 3ª ed. Pearson Prentice Hall, São Paulo.
Jiang, J.; Z. Zhang; Q. Dong e F. Ni (2018) Characterization and Identification of Asphalt Mixtures Based on Convolutional Neural Network Methods using X-ray Scanning Images. Construction and Building Materials, v. 174, p. 72–80. DOI: 10.1016/j.conbuildmat.2018.04.083. DOI: https://doi.org/10.1016/j.conbuildmat.2018.04.083
Masad, E.; B. Muhunthan; N. Shashidhar e T. Harman (1999) Quantifying Laboratory Compaction Effects on the Internal Structure of Asphalt Concrete. Transportation Research Record: Journal of the Transportation Research Board, v. 1681, pp. 179-185. DOI: 10.3141/1681-21. DOI: https://doi.org/10.3141/1681-21
Masad, E.; V. K. Jandhyala; N. Dasgupta; N. Somadevan e N. Shashidhar (2002) Characterization of Air Void Distribution in Asphalt Mixes using X-ray Computed Tomography. Journal of Materials in Civil Engineering, v. 14(2), p. 122-129. DOI: 10.1061/(asce)0899-1561(2002)14:2(122). DOI: https://doi.org/10.1061/(ASCE)0899-1561(2002)14:2(122)
Nascimento, L.; L. Leite; E. F. Campos; G. Marques e L. Motta (2006) Uso da Tomografia Computadorizada e de Imagens Digitais para o Estudo de Misturas Asfálticas. Anais do Encontro do Asfalto IBP - 18, Rio de Janeiro.
Neto, J.; A. Fiori, A. Lopes; C. Marchese; C. Coelho; E. Vasconcellos; G. Silva e R. Secchi (2011) A Microtomografia Computadorizada de Raios X Integrada à Petrografia no Estudo Tridimensional de Porosidade em Rochas. Revista Brasileira de Geociências. v. 41(3). p. 498-508, 2011. DOI: 10.25249/0375-7536.2011413498508. DOI: https://doi.org/10.25249/0375-7536.2011413498508
Osmari, P. H.; R. F. Costa; F. T. S. Aragão; D. Braz, R. C. R. Barroso; L. P. Nogueira e A. K. Y. Ng (2020) Determination of Volumetric Characteristics of FAM Mixtures using X-Ray Micro-Computed Tomography and Their Effects on the Rheological Behavior of the Material. Transportation Research Record: Journal of the Transportation Research Board, v. 2674(5), p. 97-107. DOI: 10.1177/0361198120914607. DOI: https://doi.org/10.1177/0361198120914607
Palombo, L. (2017) A Microtomografia de Raios X e a Porosimetria por Intrusão de Mercúrio na Determinação de Porosidade e Densidade de Rochas Reservatório. Dissertação de Mestrado, Universidade de São Paulo, São Paulo.
Provencher, B.; N. Piché e M. Marsh (2019) Simplifying and Streamlining Large-Scale Materials Image Processing with Wizard-Driven and Scalable Deep Learning. Microscopy and Microanalysis, v. 25, S2, p. 402-403. DOI: 10.1017/s1431927619002745. DOI: https://doi.org/10.1017/S1431927619002745
Sadeq, M.; E. A. Masad; H. Al-Khalid e O. Sirin (2018) Characterisation of Air Voids in W-FAM Samples using X-Ray CT Imaging. Advances in Materials and Pavement Performance Prediction: CRC Press, p. 97-100. DOI: 10.1201/9780429457791-25. DOI: https://doi.org/10.1201/9780429457791-25
Schindelin, J.; I. Arganda-Carreras; E. Frise; V. Kaynig; M. Longair; T. Pietzsch; S. Preibisch; C. Rueden; S. Saalfeld; B. Schmid; J-Y. Tinevez; D. J. White; V. Hartenstein; K. Eliceiri; P. Tomancak e A. Cardona (2012) Fiji: an Open-source Platform for Biological-Image Analysis. Nature Methods, v. 9(7), p. 676-682. DOI: 10.1038/nmeth.2019. DOI: https://doi.org/10.1038/nmeth.2019
Souza, T. D.; A. J. Enríquez-León; C. R. R. Souza; F. T. S. Aragão; C. Logelo; I. C. Lima, M. C. Santo e P. Couto (2022) Avaliação das Características Volumétricas de Matrizes de Agregados Finos Usando Microtomografia e Segmentação por Deep Learning. XXXVI ANPET, Fortaleza.
Souza, T. D.; A. J. Enríquez-León; M. L. Rocha; P. H. Osmari; F. T. S. Aragão; A. M. B. Pereira e B. S. Underwood (2023) A Criterion to Select the Maximum Aggregate Size of Fine-Aggregate Asphalt Matrices. 102nd Annual Meeting of the Transportation Research Board (TRB). Washington, D. C., Estados Unidos. No prelo.
Shaheen, M.; A. Al-Mayah e S. Tighe (2016) A Novel Method for Evaluating Hot Mix Asphalt Fatigue Damage: X-ray Computed Tomography. Construction and Building Materials, v. 113, p. 121-133. DOI: 10.1016/j.conbuildmat.2016.03.030. DOI: https://doi.org/10.1016/j.conbuildmat.2016.03.030
Tashman, L.; E. Masad; J. D'Angelo; J. Bukowski e T. Harman (2002) X-ray Tomography to Characterize Air Void Distribution in Superpave Gyratory Compacted Specimens. International Journal of Pavement Engineering, v. 3, n. 1, p. 19-28. DOI: 10.1080/10298430290029902a. DOI: https://doi.org/10.1080/10298430290029902a
Thyagarajan, S.; L. Tashman; E. Masad e F. Bayomy (2010) The Heterogeneity and Mechanical Response of Hot Mix Asphalt Laboratory Specimens. International Journal of Pavement Engineering, v. 11(2), p. 107-121. DOI: 10.1080/10298430902730521. DOI: https://doi.org/10.1080/10298430902730521
Underwood, B. S. (2011) Multiscale Constitutive Modeling of Asphalt Concrete. Tese de Doutorado. North Carolina State University, Carolina do Norte.
Landis, E e D. Keane (2010) X-ray microtomography. Materials Characterization, v. 61, n. 12, p. 1305-1316, DOI: 10.1016/j.matchar.2010.09.012. DOI: https://doi.org/10.1016/j.matchar.2010.09.012
Yang, H.; J. Huyan; T. Ma; Z. Tong; C. Han e T. Xie (2022) Novel Computer Tomography Image Enhancement Deep Neural Networks for Asphalt Mixtures. Construction and Building Materials, v. 352, 129067, p. 1-13. DOI: 10.1016/j.conbuildmat.2022.129067. DOI: https://doi.org/10.1016/j.conbuildmat.2022.129067
You, Z.; S. Adhikari e M. Emin Kutay (2008) Dynamic Modulus Simulation of the Asphalt Concrete using the X-ray Computed Tomography Images. Materials and Structures, v. 42, n. 5, p. 617-630, DOI: 10.1617/s11527-008-9408-4. DOI: https://doi.org/10.1617/s11527-008-9408-4
Zelelew, H. M.; A. Almuntashri; S. Agaian e A. T. Papagiannakis (2013) An Improved Image Processing Technique for Asphalt Concrete X-ray CT Images. Road Materials and Pavement Design, v. 14, n. 2, p. 341-359, DOI: 10.1080/14680629.2013.794370. DOI: https://doi.org/10.1080/14680629.2013.794370
Zhao, Z.; J. Jiang; F. Ni e X. Ma (2021) 3D-Reconstruction and Characterization of the Pore Microstructure within the Asphalt FAM using the X-ray Micro-computed Tomography. Construction and Building Materials, v. 272, 121764, p. 1-9. DOI: 10.1016/j.conbuildmat.2020.121764. DOI: https://doi.org/10.1016/j.conbuildmat.2020.121764
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Thiago Delgado de Souza, Alexis Jair Enríque-León, Francisco Thiago Sacramento Aragão, André Maués Brarbo Pereira, Liebert Parreiras Nogueira
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who submit papers for publication by TRANSPORTES agree to the following terms:
- Authors retain copyright and grant TRANSPORTES the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors may enter into separate, additional contractual arrangements for the non-exclusive distribution of this journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in TRANSPORTES.
- Authors are allowed and encouraged to post their work online (e.g., in institutional repositories or on their website) after publication of the article. Authors are encouraged to use links to TRANSPORTES (e.g., DOIs or direct links) when posting the article online, as TRANSPORTES is freely available to all readers.
- Authors have secured all necessary clearances and written permissions to published the work and grant copyright under the terms of this agreement. Furthermore, the authors assume full responsibility for any copyright infringements related to the article, exonerating ANPET and TRANSPORTES of any responsibility regarding copyright infringement.
- Authors assume full responsibility for the contents of the article submitted for review, including all necessary clearances for divulgation of data and results, exonerating ANPET and TRANSPORTES of any responsibility regarding to this aspect.
